The objective of this project was to link genes and metabolites in order to ultimately predict which metabolites to measure in order to adequately reflect the function of a given gene. Specifically, we were interested in genes, which code for proteins that regulate substrate metabolism, hence enzymes that catalyze reactions that are part of metabolic pathways. In order to quantify this link, we have developed a bioinformatics method to calculate a distance, which is defined as the number of reactions separating a given selected gene-encoded enzyme and its metabolite of interest in Kyoto Encyclopedia of Genes and Genomes (KEGG) database’s metabolic overview map. Our hypothesis was that metabolites of interest are products/substrates found at proximity of the reactions catalyzed by the selected gene-encoded enzyme. In order to test our hypothesis and validate the method, we have used genome-wide association study of metabolites levels (mGWAS) because these studies report associations between genetic variants, annotated to genes, and measured metabolites. More specifically, we used the mGWAS conducted by Shin et al. Even though the coverage of the associations reported by Shin et al. was limited (24/299), we significantly validated the proximity between gene-metabolite associated pairs (P<0.01). Overall, the method and its future developments will allow the quantitative interpretation of mGWAS associations, predict which metabolite to measure with regards to the function of a gene and, in general, enable a better understanding of the genetic control of metabolism.